Feature Selection and Classification - A Probabilistic Wrapper Approach
نویسندگان
چکیده
Feature selection is de ned as a problem to nd a minimum set of M features for an inductive al gorithm to achieve the highest predictive accuracy from the data described by the original N features where M N A probabilistic wrapper model is proposed as another method besides the exhaus tive search and the heuristic approach The aim of this model is to avoid local minima and exhaustive search The highest predictive accuracy is the crite rion in search of the smallest M Analysis and ex periments show that this model can e ectively nd relevant features and remove irrelevant ones in the context of improving the predictive accuracy of an induction algorithm It is simple straightforward and providing fast solutions while searching for the optimal The applications of such a model its future work and some related issues are also discussed
منابع مشابه
Fuzzy-rough Information Gain Ratio Approach to Filter-wrapper Feature Selection
Feature selection for various applications has been carried out for many years in many different research areas. However, there is a trade-off between finding feature subsets with minimum length and increasing the classification accuracy. In this paper, a filter-wrapper feature selection approach based on fuzzy-rough gain ratio is proposed to tackle this problem. As a search strategy, a modifie...
متن کاملEnsemble Classification and Extended Feature Selection for Credit Card Fraud Detection
Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost for accuracy enhancement of credit card fraud detection. After selecting the best and most effec...
متن کاملFast SFFS-Based Algorithm for Feature Selection in Biomedical Datasets
Biomedical datasets usually include a large number of features relative to the number of samples. However, some data dimensions may be less relevant or even irrelevant to the output class. Selection of an optimal subset of features is critical, not only to reduce the processing cost but also to improve the classification results. To this end, this paper presents a hybrid method of filter and wr...
متن کاملDeveloping a Filter-Wrapper Feature Selection Method and its Application in Dimension Reduction of Gen Expression
Nowadays, increasing the volume of data and the number of attributes in the dataset has reduced the accuracy of the learning algorithm and the computational complexity. A dimensionality reduction method is a feature selection method, which is done through filtering and wrapping. The wrapper methods are more accurate than filter ones but perform faster and have a less computational burden. With ...
متن کاملFeature Selection Using Multi Objective Genetic Algorithm with Support Vector Machine
Different approaches have been proposed for feature selection to obtain suitable features subset among all features. These methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. The objective functions are divided into two main groups: filter and wrapper methods. In filter methods, features subsets are selected due to some measu...
متن کاملBridging the semantic gap for software effort estimation by hierarchical feature selection techniques
Software project management is one of the significant activates in the software development process. Software Development Effort Estimation (SDEE) is a challenging task in the software project management. SDEE is an old activity in computer industry from 1940s and has been reviewed several times. A SDEE model is appropriate if it provides the accuracy and confidence simultaneously before softwa...
متن کامل